Synthetic data generation creates artificial training data using generative models to augment or replace real data. Motivations: Insufficient real data, privacy constraints, rare event simulation, cost of data collection, label automation. Approaches: LLM generation: Prompt models to generate text examples, dialogues, QA pairs. Diffusion models: Generate images with controlled attributes. GANs: Generate realistic samples from learned distribution. Simulation: Physics/rule-based synthetic environments. For NLP: Generate paraphrases, translations, domain-specific text, instruction-response pairs, code examples. Quality considerations: Diversity (avoid mode collapse), fidelity (realistic enough to be useful), correct labels, avoiding generating training data artifacts. Self-instruct pattern: LLM generates instruction-following examples for its own training. Bootstrapping: Generate data, train model, use improved model to generate better data. Challenges: Distribution shift from real data, error amplification, potential for generating harmful content. Tools: OpenAI API, Alpaca-style generation, Evol-Instruct. Increasingly central to LLM training pipelines.